PsLSNet: Automated psoriasis skin lesion segmentation using modified U-Net-based fully convolutional network

2019 ◽  
Vol 52 ◽  
pp. 226-237 ◽  
Author(s):  
Manoranjan Dash ◽  
Narendra D. Londhe ◽  
Subhojit Ghosh ◽  
Ashish Semwal ◽  
Rajendra S. Sonawane
Keyword(s):  
Skin Lesion ◽  
Lesion Segmentation ◽  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation

2020 ◽  
Vol 10 (10) ◽  
pp. 3658
Author(s):  
Karshiev Sanjar ◽  
Olimov Bekhzod ◽  
Jaeil Kim ◽  
Jaesoo Kim ◽  
Anand Paul ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Interpolation Method ◽  
Medical Personnel ◽  
Activation Function ◽  
Lesion Segmentation ◽  
Bilinear Interpolation ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Computer Vision Applications ◽  
Parametric Rectified Linear Unit

The early and accurate diagnosis of skin cancer is crucial for providing patients with advanced treatment by focusing medical personnel on specific parts of the skin. Networks based on encoder–decoder architectures have been effectively implemented for numerous computer-vision applications. U-Net, one of CNN architectures based on the encoder–decoder network, has achieved successful performance for skin-lesion segmentation. However, this network has several drawbacks caused by its upsampling method and activation function. In this paper, a fully convolutional network and its architecture are proposed with a modified U-Net, in which a bilinear interpolation method is used for upsampling with a block of convolution layers followed by parametric rectified linear-unit non-linearity. To avoid overfitting, a dropout is applied after each convolution block. The results demonstrate that our recommended technique achieves state-of-the-art performance for skin-lesion segmentation with 94% pixel accuracy and a 88% dice coefficient, respectively.

scholarly journals Dense pooling layers in fully convolutional network for skin lesion segmentation

2019 ◽  
Vol 78 ◽  
pp. 101658 ◽  
Author(s):  
Ebrahim Nasr-Esfahani ◽  
Shima Rafiei ◽  
Mohammad H. Jafari ◽  
Nader Karimi ◽  
James S. Wrobel ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Lesion Segmentation ◽  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals Melanoma Recognition and Lesion Segmentation using Multi-Instance Learning

2021 ◽  
Author(s):  
Yangling Ma ◽  
Zhouwang Yang
Keyword(s):  
Skin Lesion ◽  
Coupled Processes ◽  
Lesion Segmentation ◽  
Image Block ◽  
Accurate Identification ◽  
Convolutional Network ◽  
Melanoma Diagnosis ◽  
High Resolution Images ◽  
End To End ◽  
Classification Probability

Abstract Melanoma is one of the deadliest forms of skin cancer, but early and accurate identification can significantly improve the survival rate of patients. In this paper, an end-to-end framework based on multi-instance learning is proposed for melanoma recognition and lesion segmentation simultaneously. To make full use from the information of high-resolution images, we take each image block (super-pixel) as an instance in a bag and use multi-instance learning based on a graph convolutional network to recognize melanoma. Moreover, skin lesion segmentation is derived from attention weights and is calibrated by classification probability vectors. As a result, the AUC of our method for melanoma recognition reaches 0.93, which is much higher compared with other related methods. Also, the Jaccard index (JA) of our method for melanoma-related skin lesion segmentation reaches 0.699. In our end-to-end approach, segmentation and recognition are treated as intimately coupled processes, and hence, a high JA is also an indication of the reliability of melanoma recognition. Collectively, these findings confirmed that our method effectively assists melanoma diagnosis.

scholarly journals Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

2020 ◽  
Vol 39 (3) ◽  
pp. 169-185
Author(s):  
Omran Salih ◽  
Serestina Viriri
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Skin Lesions ◽  
Training Dataset ◽  
Great Success ◽  
Lesion Segmentation ◽  
Convolutional Network ◽  
Low Contrast

Deep learning techniques such as Deep Convolutional Networks have achieved great success in skin lesion segmentation towards melanoma detection. The performance is however restrained by distinctive and challenging features of skin lesions such as irregular and fuzzy border, noise and artefacts presence and low contrast between lesions. The methods are also restricted with scarcity of annotated lesion images training dataset and limited computing resources. Recent research in convolutional neural network (CNN) has provided a variety of new architectures for deep learning. One interesting new architecture is the local binary convolutional neural network (LBCNN), which can reduce the workload of CNNs and improve the classification accuracy. The proposed framework employs the local binary convolution on U-net architecture instead of the standard convolution in order to reduced-size deep convolutional encoder-decoder network that adopts loss function for robust segmentation. The proposed framework replaced the encoder part in U-net by LBCNN layers. The approach automatically learns and segments complex features of skin lesion images. The encoder stage learns the contextual information by extracting discriminative features while the decoder stage captures the lesion boundaries of the skin images. This addresses the issues with encoder-decoder network producing coarse segmented output with challenging skin lesions appearances such as low contrast between healthy and unhealthy tissues and fine grained variability. It also addresses issues with multi-size, multi-scale and multi-resolution skin lesion images. The deep convolutional network also adopts a reduced-size network with just five levels of encoding-decoding network. This reduces greatly the consumption of computational processing resources. The system was evaluated on publicly available dataset of ISIC and PH2. The proposed system outperforms most of the existing state-of-art.

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